Proline tRNA synthetase inhibitors discovered to date have all been knowledge-based and sampled an extremely small portion of chemical property space and compounds identified this way have had negligible clinical impact against infectious diseases. During this period, the project team conducted quantitative high-throughput screening (qHTS) on select small molecule libraries, and hit compounds were validated. Machine learning approaches were utilized to construct in silico quantitative structure-activity relationship (QSAR) models to enable virtual screening of compounds against larger collections of NCATS molecules. Work is ongoing screening larger compound collections to identify compounds with increased potency and enable refinement of the QSAR model.